Mapping AI use in the human brain: divergent neural, academic, and mental-health profiles

Junji Wang's dataset from 2026 combines survey data and high-resolution structural MRI scans from 222 university students. It examines patterns of functional and socio-emotional AI conversational agent (AICA) use and their relationships with academic performance, mental health, and brain structure. The data was published on figshare under a CC-BY-4.0 license.

Use Cases

• Correlating AI usage frequency with academic GPA based on survey data.
• Analyzing associations between socio-emotional AI use and depression/social anxiety scores.
• Investigating links between brain region volumes (e.g., dorsolateral prefrontal cortex, amygdala) and specific AI use patterns.

Strengths

• Includes data from 222 participants, a comparatively large sample for neuroimaging studies.
• Combines multiple data modalities: survey responses and high-resolution structural MRI scans.
• Applies multiple analysis levels: computational-anatomy, meta-analytic, and network-level.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count for the underlying tabular data is unknown, which may limit suitability assessment.
• The sample is specific to university students, which may limit generalizability.

Provenance

Source

figshare, author Junjie Wang

Collection Method

Combined surveys and high-resolution structural MRI scans.

Freshness

Last updated 2026-05-19 11:29:48; freshness should be verified.